Statistical methods for survival data analysis

Book by Lee, Elisa T.

The Third Edition of the leading reference on survival data analysis

The study of survival data attempts to predict the probability of response, survival, or mean lifetime; compare; the survival distributions of experimental animals or of human patients; and identify risk and/or prognosis factors. Statistical Methods for Survival Data Analysis, Third Edition examines the statistical methods for analyzing survival data from laboratory studies of animals, clinical and epidemiological studies of humans, and other appropriate applications.

Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussions of the most commonly used parametric and nonparametric methods in survival analysis, as well as guidelines for the planning and design of clinical trials. The authors give special consideration to the study of survival data in biomedical sciences, though the methods are suitable for applications in industrial reliability, the social sciences, and business.

This Third Edition brings this standard in the field up to date with new material and revised references including:

• A new introduction to left and interval censored data
• The generalized gamma and log-logistic distribution
• Estimation procedures for left and interval censored data
• Parametric models with covariates
• Cox’s proportional hazards model including stratification and time-dependent covariates, and some non-proportional hazards models
• Goodness-of-Fit tests and model selection methods
• Multiple responses to the logistic regression model
• Numerous real-life examples which illustrate key concepts
• Computer programming codes in SAS, BMDP, and SPSS for most examples
• Related FTP site providing large data sets

These additions and revisions make Statistical Methods for Survival Data Analysis, Third Edition, more valuable than ever as an essential reference for biomedical investigators, statisticians, epidemiologists, and researchers in other disciplines involved or interested in the analysis of survival data.